Three fundamental topics in modelling and simulation are studied in this study-unit - Input Modelling (derivation of a model from data), stochastic and discrete event Simulation Paradigms and Analysis of the Output data.

An applied approach is adopted and practical examples that reinforce the techniques studied are cited and discussed. This study-unit is delivered from a computing point of view and it is therefore expected that the student is conversant in a high level programming language.

This unit is a useful companion/complementary unit to other units such as discrete event systems, networks, machine learning and other courses in computer science and engineering.

Study-unit Aims:

- To learn the modelling methodology;- To study the most useful and widely applicable modelling techniques;- To study the most useful and widely applicable stochastic and discrete event computer simulation techniques;- To learn how to develop good and robust computational models;- To explore qualitatively and in a wider context the area of modelling and computer simulation.

Learning Outcomes:

1. Knowledge & Understanding:By the end of the study-unit the student will be able to:

- Differentiate between dynamic and static models;- Differentiate between discrete event systems and continuous-time systems;- Specify a random number generator and derive the maths models required to generate random variables and model inputs;- Test a random number generator;- Code static modelling techniques in a high level computer language;- Code a discrete event simulation in a high level language;- Formulate a Monte-Carlo simulation and analyse the output;- Solve stochastic problems analytically;- Compare simulation results to analytical results;- Manually recognize patterns in data and select a model that fits the pattern;- Understand the difference between linear regression and logistic regression models;- Numerically fit a regression model to data;- Understand the local and global optimisation problem;- Derive and Implement a gradient based minimisation method;- Formulate a probabilistic model in a simple classification problem;- formulate a probabilistic Markov model for times series modelling;- Implement a simple times series model that fits data;- Differentiate between time-domain Data-Driven Models and a Physics Models;- Formulate a simple difference equation model motivated by qualitative analysis and verify the model;- Numerically compute the difference equation coefficients;- Compare the complexity of the difference equation model to that of a physics-based model;- Implement a continuous-time system model on a discrete event simulator as a mixed mode simulation environment;- Analyse and validate the simulation output;- Provide a measure of accuracy for the output data.

2. Skills:By the end of the study-unit the student will be able to:

- Write down and elaborate on the problem definition and abstraction of a model;- Adapt an algorithm or algorithms or methods that can be computed on a digital computer;- Validate, Optimize and Tune a model;- Provide a critical review of the model output;- Coding the algorithm/s in Python.

The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the study-unit description above applies to the academic year 2017/8, if study-unit is available during this academic year, and may be subject to change in subsequent years.